Summer 2023
Providing a data visualization that presents a comprehensive investigation into the impact of climate change between the years 1750 and 2015, employing temporal and trend-line mapping techniques to explore the evolution of climate-related parameters, and provide discernment for the individual factors influencing climate change over time and their respective influence. The objective of this data visualization is to enhance understanding of the drivers of climate change, enabling more accurate projections of future climate scenarios. Considering the prevailing climate emergency, a motivation for this data visualization project is to provide valuable insights towards cultivating a more environmentally-conscious world.
(Climate Change, Geographic Data, Atmospheric Science, Time series data)
The question that this project will answer is how climate temperature has changed over time. Climate change is an important issue globally, and being able to understand the progression of climate change is important data for creating action. To address this question, a series of visualizations derived from a dataset will be created; a temporal map, showing a heatmap laid over a world map, a bubble chart that represents the difference in heat by year, an area graph representing temperature over time with a slider to alter the time interval, and a line chart that depicts the overall velocity of temperature change over time from 1750 to now, knowing that earth temperatures have been relatively stable before 1750 throughout recent human history. The velocity at which the temperature is changing overall is also of concern, because it can be used to set the trendline for making future predictions. As a result, there are many factors the data provides that are worth identifying such as:
Finding answers to these questions is important since they will ensure scientific accountability, raise awareness in large audiences, maintain documentation of historical data, and hopefully advocate for international collaboration. It is important to consider that this data set has some limitations; the data dates back to 1750, all the way up to 2015. While this is a large dataset, it isn’t necessarily the most updated representation of the climate today since it excludes the previous eight years of data. Therefore a trendline for future predictions may be helpful to use and compare with current temperatures for accuracy. This dataset lacks granular data for every place on the globe, a limitation of this dataset. The missing data is notable because it inhibits the accuracy of the data representation and it will be countered by supplying more refined data on the location and date of significant temperature changes over time by cities.
Dataset biography:
This data was found on Kaggle. The data set can be found here: Climate Change Data Set
The data was collected by Berkeley Earth and Kristen Sissener
Data collection methodology:
There are several different ways this data was collected because the data dates all the way back to 1750, it is difficult to have consistent measurement. Early periods of temperature data were gathered using mercury thermometers, post 1940’s construction moved weather stations around, and the 1980’s introduced a device known to have a cooling bias; digital thermometers. There were 16 different data archives with a whopping 1.6 billion individual temperature records reported. Berkeley Earth divided the data into categories based on min, max, confidence intervals, global scale, by specific location, and by date.
This data was collected to identify location indicators affecting climate temperature change over time. Authors Berkeley Earth and Kristen Sissener acknowledge the ongoing debate on whether climate change is a real issue, and collected this wide range of temporal data by location in order to represent a seemingly unbiased data set for analysis. By providing data based on location rather than population per capita, the data eradicates any room for socioeconomic debate and only provides space to analyze the correlation between date and location in temporal history.
Dimensions of dataset
| Dimensions | global_temp | country_temp | annual_city_temp |
|---|---|---|---|
| Rows | 3192 | 577462 | 714487 |
| Columns | 9 | 4 | 9 |
Some ethical questions considered when working with this data include…
Is the data representative of the global population?
Does the data have a motive or an agenda behind it?
It is important to consider that this data set has some limitations; the data dates back to 1750, all the way up to 2015. While this is a large data-set, it isn’t necessarily the most updated representation of the climate today since it excludes the previous six years of data. Therefore a trend-line for future predictions may be helpful to use and compare with current temperatures for accuracy. This data-set also lacks granular data for all surface locations on the globe, another limitation of this data-set. The missing data is notable because it inhibits the accuracy of the data representation.
When addressing the challenge of climate change, technologists, designers, and Policymakers are the result of addressing tangible solutions for the future of climate change and how data about it is understood. The fusion of innovative technologies with sustainable design that is supported and enforced by effective policies ensures the acceleration of the ways sustainable practices across the globe will ensure a more environmentally-conscious future.
Possible limitations or problems when working with this particular dataset are concerns of the locations being represented. Knowing that this data set does not have granular time series data for each individual city, state, or country as it is aggregated monthly. It is important to consider how this presents difficulties when deciphering events with the highest or lowest impact on the climate. Alternatively, the information provided comes from a multitude of different measurement sources dating all the way back to the pre-industrial revolution. The ways in which temperature measurements are gathered today are different than they were in 1750, and the results of these measurements reflect that. The authors of this data set mention that they collected data from measurements based off of very dated instruments, such as mercury thermometers of the pre-industrial age, and the digital thermometers used in the late 1900’s, which are known to have a cooling bias.
These concerns question not only the integrity of the data’s accuracy and precision, but also the invisible power structures that it represents. It i s clear not every location was capable of being held accountable for proper measuring techniques, therefore the data collected must consider those disparities. There is missing data for many countries and cities before 1850, there is even more missing data for Antarctica. Additionally, considering the main question to answer is how temperature of the climate has changed over time, the data limits or completely dismisses the ability to extract individual variables that also represent environmental indicators of significant climate change such as ocean heat levels or snow melt in the spring. This is important because these factors are crucial in determining temperature change over time, arguably more so than analyzing indicators based on location.
Between the years 1750 and 2015, the average global temperature has experienced a significant change, with an average increase of approximately 1.11°C. In 1752, the minimum average temperature was recorded at 5.78°C, while in 2015, the maximum average temperature reached 9.83°C. A notable temperature of 8.38°C was observed in 1906 as the median average temperature during this period. Finally, the mean of these temperatures can be calculated: \[\bar{X} = \frac{{1752 \times 5.779833 + 2015 \times 9.831 + 1906 \times 8.379083}} {{1752 + 2015 + 1906}}\]. This equation gives a mean temperature finding of approximately 8.04 degrees Celsius. Overall, from the given values found in the data set, the overall mean temperature for the years ranging from 1750-2015 is roughly 8.04°C.
The data shows:
From the data visualizations, it is clear that temperatures increased across the globe since 1750. the greatest changes in temperature occurred in cities located in northern Asian and East Europe. Furthermore, the velocity of temperature change has been consistently above 0°C from 1750 to 2015 meaning average temperatures have been increasing yearly. Temperature change was also decelerating from 1750 to around 1930, but from 1930 to 2015, temperature change suddenly began to accelerate, around the same time as the automobile became common. Over time the uncertainty of data begins to steadily decrease as technology for temperature collection tools improved continuously. While North America and Asia have similar latitudes, the difference between average temperature varies significantly with Asia at around ~10°C while North America has an average temperature at around ~5°C.
This table of Global Temperature Summary Statistics is included to give a more detailed understanding of the severity of climate change, this table includes average land and sea temperatures as well as the aggregated confidence interval for each calculation.
| Date | Event | Land Temp | Temp Confidence | Max Temp | Max Temp Confidence | Min Temperature | Min Temp Confidence |
|---|---|---|---|---|---|---|---|
| 1750 - 2015 | chg_avg_temp | 1.11 | -2.55 | 20.90 | 0.11 | -1.52 | 0.14 |
| 1750 - 2015 | avg_temp | 8.37 | 0.95 | 20.09 | 0.48 | -3.07 | 0.43 |
| 1752 | min_avg_temp | 5.78 | 2.98 | NA | NA | NA | NA |
| 1906 | med_avg_temp | 8.38 | 0.27 | 20.06 | 0.38 | -3.64 | 0.34 |
| 2015 | max_avg_temp | 9.83 | 0.09 | 20.90 | 0.11 | -1.52 | 0.14 |
The two maps included provide an overview of the current situation of climate data as of 2015. The Average Temperature By Country visualization shows average temperatures of each country around the world in Celsius (2015). The second visualization, City Temperature Changes, provides an idea of changes of temperature over time. The map shows average temperature changes in each city from 1850 to 2013 since reliable data outside that range was not included in the Berkeley Data.
The first visualization shows that countries near the equator currently have higher temperatures than countries near the poles (an interactive version is planned including a slider widget in shiny to visualize other years). The second visualization shows average change in temperature between 1850 and 2013 in major cities (with another interactive time interval widget planned in shiny). While temperatures increased across the globe, the greatest changes in temperature occurred in cities located in northern Asia and eastern Europe.
This scatter plot, Velocity in Temperature Change Over Time, is included to show the velocity of temperature change over the years along with the certainty of the data. A loess regression is also included to help visualize the change in velocity of temperature change over time (acceleration of temperature change). This can provide insight on not only the overall trend of temperature change, but also show changes in temperature collection technology.
From the plot “Velocity in Temperature Change Over Time”, the velocity of temperature change is consistently above zero, meaning that it is on average increasing yearly while the uncertainty of data is steadily decreasing over time as technological advancements are made. Note that the recorded temperatures before 1850 have confidence intervals that would not be able to detect a one degree change in global temperature. Temperature change was decelerating from 1750 to around 1930, however global temperatures were still increasing. From 1930 to 2015, temperature change began to accelerate. At this rate of acceleration, it is no surprise 2023 is shaping up to be the hottest year in 150,000 years.
The plot Average Temperature by Continent, is included to compare average temperatures of each continent from 1850-2013. This comparison can allow for more detailed examination of differences in temperature between continents that are a similar distance from the equator.
This visualization shows that while North America and Asia have similar latitudes, the difference between average temperature varies significantly with Asia at around ~10C while North America has an average temperature at around ~5C. As with the heat map of the world, there are plans to enhance the amount of insight this visualization can give with widgets from shiny to include custom time intervals.
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